Xingchen Li

Diagnostic significance and predictive efficiency of metabolic risk score for fertility-sparing treatment in patients with atypical endometrial hyperplasia and early endometrial carcinoma

This study aims to assess the impact of the metabolic risk score (MRS) on time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early endometrial cancer (EC) patients. Univariate and multivariate cox analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan-Meier analysis was utilized to determine the cumulative CR rate for patients. The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=-2.16; 95% CI=-4.03, -0.28; p=0.025), and histological type (HR=0.36; 95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan-Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups. MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

Identification of an immune-related risk signature and nomogram predicting the overall survival in patients with endometrial cancer

Aimed to construct an immune-related risk signature and nomogram predicting endometrial cancer (EC) prognosis. An immune-related risk signature in EC was constructed using the least absolute shrinkage and selection operator regression analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A nomogram integrating the immune-related genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve to predict the overall survival (OS) of EC patients. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) R tool was used to explore the immune and stromal scores. CCL17, CTLA4, GPI, HDGF, HFE2, ICOS, IFNG, IL21R, KAL1, NR3C1, S100A2, and S100A9 were used in developing an immune-related risk signature evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had better OS (p<0.001). The area under the ROC curve (AUC) values of this model were 0.737, 0.764, and 0.782 for the 3-, 5-, and 7-year OS, respectively. A nomogram integrating the immune-related risk model and clinical features could accurately predict the OS (AUC=0.772, 0.786, and 0.817 at 3-, 5-, and 7-year OS, respectively). The 4 immune cell scores were lower in the high-risk group. Forkhead box P3 (FOXP3) and basic leucine zipper ATF-like transcription factor (BATF) showed a potential significant role in the immune-related risk signature. Twelve immune-related genes signature and nomogram for assessing the OS of patients with EC had a good practical value.

Lipid‐Driven OLR1/FOXM1/FGF19 Axis Orchestrates Crosstalk in an Epithelial‐Fibroblast Positive Feedback Promoting Progesterone Resistance in Endometrial Cancer

Abstract Progesterone resistance (ProR) remains a major obstacle in the conservative management of endometrial cancer (EC). Here, a metabolic‐stromal signaling loop centered on the OLR1/FOXM1/FGF19 axis is identified that drives progesterone resistance in EC. Single‐cell transcriptomic profiling first revealed a striking correlation between epithelial cells and fibroblasts in EC tissues with ProR. Tumor epithelial cells display profound alterations in lipid metabolism, whereas fibroblasts exhibited enhanced oxidative stress signatures. Clinical samples analyses indicated that oxidized low density lipoprotein (oxLDL), a product of LDL oxidation, is associated with adverse outcomes. The binding of oxLDL to its receptor OLR1 promoted the expression of FOXM1, a transcription factor that directly upregulates fibroblast growth factor 19 (FGF19). Immunofluorescence confirmed not only the spatial co‐localization of epithelial cells and fibroblasts but also the enrichment of OLR1 within epithelial compartments. Furthermore, treatment with the antioxidant resveratrol (RSV) and its nanoformulation (RSV‐NPs) markedly inhibited tumor growth in mice with lipid metabolic disorders, highlighting their potential to counteract progesterone resistance by disrupting this OLR1/FOXM1/FGF19 axis. This work highlights the therapeutic potential of targeting the tumor–stroma metabolic axis to increase progesterone sensitivity and improve outcomes in EC patients with fertility‐preserving demands.

Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients

Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC. A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram. The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from -8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan-Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram. We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.

Establishment and validation of a prognostic nomogram based on a novel five‐DNA methylation signature for survival in endometrial cancer patients

AbstractBackgroundThis study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.MethodsDifferentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.ResultsIn total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5‐7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow‐up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062–8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061–4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225–9.807, p = 0.019), and five‐DNA methylation risk model (HR = 3.654, 95%CI: 1.458–9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan–Meier survival curve also proved to be significantly different (p &lt; 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p &lt; 0.01).ConclusionsBriefly, our work established and verified a five‐DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.